Loading Now

Summary of Causally Inspired Regularization Enables Domain General Representations, by Olawale Salaudeen and Sanmi Koyejo


Causally Inspired Regularization Enables Domain General Representations

by Olawale Salaudeen, Sanmi Koyejo

First submitted to arxiv on: 25 Apr 2024

Categories

  • Main: Machine Learning (cs.LG)
  • Secondary: Machine Learning (stat.ML)

     Abstract of paper      PDF of paper


GrooveSquid.com Paper Summaries

GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!

Summary difficulty Written by Summary
High Paper authors High Difficulty Summary
Read the original abstract here
Medium GrooveSquid.com (original content) Medium Difficulty Summary
This paper explores how to identify domain-general feature representations from a given causal graph representing the data-generating process across different domains. By enforcing sufficient graph-implied conditional independencies, it is possible to identify these non-spurious features. The authors categorize existing methods into two groups: those that naturally provide domain-general representations and those that do not. For the latter case, they propose a novel framework with regularizations that can identify domain-general feature representations without prior knowledge of spurious features. Experimental results demonstrate the effectiveness of this approach on both synthetic and real-world data, outperforming other state-of-the-art methods in average and worst-domain transfer accuracy.
Low GrooveSquid.com (original content) Low Difficulty Summary
This paper is about how to get useful information from a graph that shows how different things are connected. It wants to find features that work across many different situations, not just one special case. The authors look at what others have done and divide it into two groups: some methods naturally give good results, while others don’t. For those that don’t, they suggest a new way of doing things that works better without needing extra information about what’s important or what’s not. They test this new approach with fake and real data and show that it does better than other ways of doing things.

Keywords

» Artificial intelligence